Scientists frequently implement data analyses in high-level programming languages such as Python, Perl, Lu, and R. Many of these languages are inefficient due to the overhead of being dynamically typed and interpreted. In this paper, we report the potential performance improvement of domain-specific interpreter specialization for data analysis workloads and evaluate how the characteristics of data analysis workloads affect the specialization, both positively and negatively. Assisted by compilers, we specialize the Lu and CPython interpreters at source-level using the script being interpreted and the data types during the interpretation as invariants for five common tasks from real data analysis workloads. Through experiments, we measure 9.0–39.6% performance improvement for Lu and 11.0–17.2% performance improvement for CPython for benchmarks that perform data loading, histogram computation, data filtering, data transformation, and dataset shuffle. This specialization does not include misspeculation checks of data types at possible type conversion code that may be necessary for other workloads. We report the details of our evaluation and present a semi-automatic method for specializing the interpreters.